Multi-modality Large Deformation Diffeomorphic Metric Mapping Driven by Single-modality Images

Multi-modality magnetic resonance image (MRI) registration is an essential step in various MRI analysis tasks. However, it is challenging to have all required modalities in clinical practice, and thus the application of multi-modality registration is limited. This paper tackles such problem by proposing a novel unsupervised deep learning based multi-modality large deformation diffeomorphic metric mapping (LDDMM) framework which is capable of performing multi-modality registration only using single-modality MRIs. Specifically, an unsupervised image-to-image translation model is trained and used to synthesize the missing modality MRIs from the available ones. Multi-modality LDDMM is then performed in a multi-channel manner. Experimental results obtained on one publicly-accessible datasets confirm the superior performance of the proposed approach. Clinical relevance-This work provides a tool for multi-modality MRI registration with solely single-modality images, which addresses the very common issue of missing modalities in clinical practice.